This is one reason why I think for more general intelligence we want to pair up more computer-natural computer-friendly modeling mechanisms with leading edge ne

This is one reason why I think for more general intelligence we want to pair up more computer-natural computer-friendly modeling mechanisms with leading edge neural network architectures. For example, for computer vision, leverage efficient 3d graphics techniques or knowledge rather than relying entirely on CNNs (for example). Especially take the case of indoor environments which contain mostly man-made objects that are highly regular. Use neural networks to help detect these shapes and then perhaps iteratively refine them. But that high level structure provides a very efficient framework that significantly reduces the amount of computation required. Some people working on more general purpose AI such as Tenenbaum have been saying something at least a little bit like that for quite a long time. And it's true that although there is a lot of interesting progress, the gap remains significant between human and AI capability in terms of generalization. But I think if you look at the computational numbers suggested by the type of research in this post, and then look at the progress on generalizing AI, the achievements are not insignificant.